Overview

Dataset statistics

Number of variables11
Number of observations5473
Missing cells0
Missing cells (%)0.0%
Duplicate rows38
Duplicate rows (%)0.7%
Total size in memory470.5 KiB
Average record size in memory88.0 B

Variable types

Numeric10
Categorical1

Alerts

Dataset has 38 (0.7%) duplicate rowsDuplicates
0 is highly correlated with 2 and 3 other fieldsHigh correlation
1 is highly correlated with 2 and 4 other fieldsHigh correlation
2 is highly correlated with 0 and 5 other fieldsHigh correlation
3 is highly correlated with 1 and 4 other fieldsHigh correlation
4 is highly correlated with 5 and 1 other fieldsHigh correlation
5 is highly correlated with 4High correlation
6 is highly correlated with 4High correlation
7 is highly correlated with 0 and 5 other fieldsHigh correlation
8 is highly correlated with 0 and 5 other fieldsHigh correlation
9 is highly correlated with 0 and 5 other fieldsHigh correlation
0 is highly correlated with 2 and 2 other fieldsHigh correlation
1 is highly correlated with 3 and 2 other fieldsHigh correlation
2 is highly correlated with 0 and 3 other fieldsHigh correlation
3 is highly correlated with 1High correlation
4 is highly correlated with 5High correlation
5 is highly correlated with 4High correlation
7 is highly correlated with 0 and 3 other fieldsHigh correlation
8 is highly correlated with 0 and 4 other fieldsHigh correlation
9 is highly correlated with 1 and 3 other fieldsHigh correlation
1 is highly correlated with 2 and 4 other fieldsHigh correlation
2 is highly correlated with 1 and 4 other fieldsHigh correlation
3 is highly correlated with 1 and 4 other fieldsHigh correlation
7 is highly correlated with 1 and 4 other fieldsHigh correlation
8 is highly correlated with 1 and 4 other fieldsHigh correlation
9 is highly correlated with 1 and 4 other fieldsHigh correlation
0 is highly correlated with 2 and 3 other fieldsHigh correlation
1 is highly correlated with 3 and 1 other fieldsHigh correlation
2 is highly correlated with 0 and 3 other fieldsHigh correlation
3 is highly correlated with 1 and 1 other fieldsHigh correlation
4 is highly correlated with 5 and 1 other fieldsHigh correlation
5 is highly correlated with 4 and 1 other fieldsHigh correlation
6 is highly correlated with 3High correlation
7 is highly correlated with 0 and 4 other fieldsHigh correlation
8 is highly correlated with 0 and 3 other fieldsHigh correlation
9 is highly correlated with 0 and 4 other fieldsHigh correlation
10 is highly correlated with 4 and 2 other fieldsHigh correlation
0 is highly skewed (γ1 = 20.37000095) Skewed
6 is highly skewed (γ1 = 67.42540616) Skewed

Reproduction

Analysis started2022-05-22 21:33:36.945092
Analysis finished2022-05-22 21:33:57.667056
Duration20.72 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

0
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct104
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.47323223
Minimum1
Maximum804
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size42.9 KiB
2022-05-22T23:33:57.909797image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q17
median8
Q310
95-th percentile20
Maximum804
Range803
Interquartile range (IQR)3

Descriptive statistics

Standard deviation18.96056392
Coefficient of variation (CV)1.810383222
Kurtosis659.6587546
Mean10.47323223
Median Absolute Deviation (MAD)2
Skewness20.37000095
Sum57320
Variance359.502984
MonotonicityNot monotonic
2022-05-22T23:33:58.098936image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8940
17.2%
9903
16.5%
7831
15.2%
10630
11.5%
6351
 
6.4%
11326
 
6.0%
5271
 
5.0%
1254
 
4.6%
12164
 
3.0%
13103
 
1.9%
Other values (94)700
12.8%
ValueCountFrequency (%)
1254
 
4.6%
287
 
1.6%
358
 
1.1%
464
 
1.2%
5271
 
5.0%
6351
 
6.4%
7831
15.2%
8940
17.2%
9903
16.5%
10630
11.5%
ValueCountFrequency (%)
8041
< 0.1%
4301
< 0.1%
3111
< 0.1%
3061
< 0.1%
3041
< 0.1%
2611
< 0.1%
2121
< 0.1%
1971
< 0.1%
1872
< 0.1%
1861
< 0.1%

1
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct452
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean89.56824411
Minimum1
Maximum553
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size42.9 KiB
2022-05-22T23:33:58.285496image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q117
median41
Q3107
95-th percentile346
Maximum553
Range552
Interquartile range (IQR)90

Descriptive statistics

Standard deviation114.7217575
Coefficient of variation (CV)1.280830708
Kurtosis4.203579984
Mean89.56824411
Median Absolute Deviation (MAD)30
Skewness2.104048249
Sum490207
Variance13161.08164
MonotonicityNot monotonic
2022-05-22T23:33:58.469370image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12126
 
2.3%
13118
 
2.2%
14113
 
2.1%
7112
 
2.0%
11105
 
1.9%
8105
 
1.9%
9103
 
1.9%
19102
 
1.9%
1896
 
1.8%
2090
 
1.6%
Other values (442)4403
80.4%
ValueCountFrequency (%)
165
1.2%
228
 
0.5%
333
 
0.6%
473
1.3%
545
0.8%
659
1.1%
7112
2.0%
8105
1.9%
9103
1.9%
1064
1.2%
ValueCountFrequency (%)
5531
 
< 0.1%
5521
 
< 0.1%
5502
 
< 0.1%
5471
 
< 0.1%
5441
 
< 0.1%
5411
 
< 0.1%
5384
 
0.1%
53713
0.2%
53612
0.2%
53522
0.4%

2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1395
Distinct (%)25.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1198.405628
Minimum7
Maximum143993
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size42.9 KiB
2022-05-22T23:33:58.659258image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile29
Q1114
median322
Q3980
95-th percentile4590
Maximum143993
Range143986
Interquartile range (IQR)866

Descriptive statistics

Standard deviation4849.37695
Coefficient of variation (CV)4.046523847
Kurtosis484.7553928
Mean1198.405628
Median Absolute Deviation (MAD)252
Skewness19.52376995
Sum6558874
Variance23516456.8
MonotonicityNot monotonic
2022-05-22T23:33:58.847428image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9665
 
1.2%
7751
 
0.9%
11249
 
0.9%
4244
 
0.8%
7242
 
0.8%
12042
 
0.8%
5642
 
0.8%
18040
 
0.7%
9839
 
0.7%
9136
 
0.7%
Other values (1385)5023
91.8%
ValueCountFrequency (%)
711
0.2%
818
0.3%
924
0.4%
1017
0.3%
116
 
0.1%
1221
0.4%
1310
0.2%
1419
0.3%
1514
0.3%
168
 
0.1%
ValueCountFrequency (%)
1439931
< 0.1%
1422901
< 0.1%
1407521
< 0.1%
983681
< 0.1%
872341
< 0.1%
819541
< 0.1%
783521
< 0.1%
722041
< 0.1%
676261
< 0.1%
457601
< 0.1%

3
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1511
Distinct (%)27.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.75397698
Minimum0.007
Maximum537
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size42.9 KiB
2022-05-22T23:33:59.035424image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.007
5-th percentile0.769
Q12.143
median5.167
Q313.625
95-th percentile46
Maximum537
Range536.993
Interquartile range (IQR)11.482

Descriptive statistics

Standard deviation30.70373722
Coefficient of variation (CV)2.232353396
Kurtosis59.00986276
Mean13.75397698
Median Absolute Deviation (MAD)3.678
Skewness6.71721425
Sum75275.516
Variance942.719479
MonotonicityNot monotonic
2022-05-22T23:33:59.230439image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2120
 
2.2%
199
 
1.8%
1.568
 
1.2%
365
 
1.2%
453
 
1.0%
1.57146
 
0.8%
543
 
0.8%
1.33336
 
0.7%
1.85735
 
0.6%
635
 
0.6%
Other values (1501)4873
89.0%
ValueCountFrequency (%)
0.0071
 
< 0.1%
0.0091
 
< 0.1%
0.0122
< 0.1%
0.0131
 
< 0.1%
0.0141
 
< 0.1%
0.0194
0.1%
0.0212
< 0.1%
0.0241
 
< 0.1%
0.0262
< 0.1%
0.0272
< 0.1%
ValueCountFrequency (%)
5371
 
< 0.1%
4131
 
< 0.1%
3791
 
< 0.1%
2881
 
< 0.1%
2831
 
< 0.1%
2791
 
< 0.1%
2781
 
< 0.1%
2772
 
< 0.1%
2693
 
0.1%
268.510
0.2%

4
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct711
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3686424265
Minimum0.052
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size42.9 KiB
2022-05-22T23:33:59.415073image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.052
5-th percentile0.156
Q10.261
median0.337
Q30.426
95-th percentile0.7762
Maximum1
Range0.948
Interquartile range (IQR)0.165

Descriptive statistics

Standard deviation0.1777567501
Coefficient of variation (CV)0.4821928714
Kurtosis3.35189289
Mean0.3686424265
Median Absolute Deviation (MAD)0.08
Skewness1.63008287
Sum2017.58
Variance0.03159746221
MonotonicityNot monotonic
2022-05-22T23:33:59.625130image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1106
 
1.9%
0.28658
 
1.1%
0.37553
 
1.0%
0.33352
 
1.0%
0.438
 
0.7%
0.35738
 
0.7%
0.537
 
0.7%
0.2534
 
0.6%
0.333
 
0.6%
0.29228
 
0.5%
Other values (701)4996
91.3%
ValueCountFrequency (%)
0.0523
0.1%
0.0553
0.1%
0.0561
 
< 0.1%
0.0571
 
< 0.1%
0.0591
 
< 0.1%
0.061
 
< 0.1%
0.0633
0.1%
0.0652
< 0.1%
0.0671
 
< 0.1%
0.071
 
< 0.1%
ValueCountFrequency (%)
1106
1.9%
0.9981
 
< 0.1%
0.9941
 
< 0.1%
0.9933
 
0.1%
0.9921
 
< 0.1%
0.992
 
< 0.1%
0.9861
 
< 0.1%
0.9851
 
< 0.1%
0.9841
 
< 0.1%
0.9831
 
< 0.1%

5
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct700
Distinct (%)12.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.785052622
Minimum0.062
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size42.9 KiB
2022-05-22T23:33:59.822804image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.062
5-th percentile0.484
Q10.679
median0.803
Q30.927
95-th percentile1
Maximum1
Range0.938
Interquartile range (IQR)0.248

Descriptive statistics

Standard deviation0.1706612788
Coefficient of variation (CV)0.2173883305
Kurtosis0.6919281143
Mean0.785052622
Median Absolute Deviation (MAD)0.124
Skewness-0.8347174978
Sum4296.593
Variance0.02912527208
MonotonicityNot monotonic
2022-05-22T23:34:00.018394image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1639
 
11.7%
0.77834
 
0.6%
0.7529
 
0.5%
0.78629
 
0.5%
0.85728
 
0.5%
0.66725
 
0.5%
0.88924
 
0.4%
0.824
 
0.4%
0.88623
 
0.4%
0.79223
 
0.4%
Other values (690)4595
84.0%
ValueCountFrequency (%)
0.0621
< 0.1%
0.0661
< 0.1%
0.071
< 0.1%
0.0711
< 0.1%
0.0891
< 0.1%
0.091
< 0.1%
0.0941
< 0.1%
0.0952
< 0.1%
0.1051
< 0.1%
0.1091
< 0.1%
ValueCountFrequency (%)
1639
11.7%
0.9993
 
0.1%
0.9984
 
0.1%
0.9977
 
0.1%
0.9968
 
0.1%
0.9953
 
0.1%
0.9945
 
0.1%
0.9937
 
0.1%
0.9927
 
0.1%
0.99116
 
0.3%

6
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct851
Distinct (%)15.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.219278275
Minimum1
Maximum4955
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size42.9 KiB
2022-05-22T23:34:00.205596image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.27
Q11.61
median2.07
Q33
95-th percentile16
Maximum4955
Range4954
Interquartile range (IQR)1.39

Descriptive statistics

Standard deviation69.07902063
Coefficient of variation (CV)11.10724067
Kurtosis4816.887815
Mean6.219278275
Median Absolute Deviation (MAD)0.59
Skewness67.42540616
Sum34038.11
Variance4771.911091
MonotonicityNot monotonic
2022-05-22T23:34:00.397517image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
293
 
1.7%
1.3859
 
1.1%
1.451
 
0.9%
1.3647
 
0.9%
1.3346
 
0.8%
1.9343
 
0.8%
743
 
0.8%
1.543
 
0.8%
1.7143
 
0.8%
1.8342
 
0.8%
Other values (841)4963
90.7%
ValueCountFrequency (%)
13
 
0.1%
1.024
 
0.1%
1.035
0.1%
1.043
 
0.1%
1.056
0.1%
1.064
 
0.1%
1.0711
0.2%
1.085
0.1%
1.094
 
0.1%
1.116
0.1%
ValueCountFrequency (%)
49551
< 0.1%
5371
< 0.1%
4121
< 0.1%
2771
< 0.1%
2571
< 0.1%
245.831
< 0.1%
2141
< 0.1%
2071
< 0.1%
2041
< 0.1%
1971
< 0.1%

7
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1069
Distinct (%)19.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean365.930751
Minimum7
Maximum33017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size42.9 KiB
2022-05-22T23:34:00.595457image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile12
Q142
median108
Q3284
95-th percentile1233.2
Maximum33017
Range33010
Interquartile range (IQR)242

Descriptive statistics

Standard deviation1270.333082
Coefficient of variation (CV)3.471512243
Kurtosis251.7609965
Mean365.930751
Median Absolute Deviation (MAD)81
Skewness13.52910554
Sum2002739
Variance1613746.139
MonotonicityNot monotonic
2022-05-22T23:34:01.037696image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
764
 
1.2%
862
 
1.1%
952
 
1.0%
1550
 
0.9%
1348
 
0.9%
2747
 
0.9%
1445
 
0.8%
1145
 
0.8%
3144
 
0.8%
2844
 
0.8%
Other values (1059)4972
90.8%
ValueCountFrequency (%)
764
1.2%
862
1.1%
952
1.0%
1032
0.6%
1145
0.8%
1237
0.7%
1348
0.9%
1445
0.8%
1550
0.9%
1639
0.7%
ValueCountFrequency (%)
330171
< 0.1%
280931
< 0.1%
278201
< 0.1%
266931
< 0.1%
230251
< 0.1%
194301
< 0.1%
185911
< 0.1%
177211
< 0.1%
142921
< 0.1%
141801
< 0.1%

8
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1718
Distinct (%)31.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean741.1081674
Minimum7
Maximum46133
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size42.9 KiB
2022-05-22T23:34:01.220251image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile24
Q195
median250
Q3718
95-th percentile2867.8
Maximum46133
Range46126
Interquartile range (IQR)623

Descriptive statistics

Standard deviation1881.504302
Coefficient of variation (CV)2.538771511
Kurtosis187.0185333
Mean741.1081674
Median Absolute Deviation (MAD)194
Skewness11.14507385
Sum4056085
Variance3540058.438
MonotonicityNot monotonic
2022-05-22T23:34:01.443557image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8928
 
0.5%
7727
 
0.5%
4225
 
0.5%
5625
 
0.5%
3525
 
0.5%
7225
 
0.5%
825
 
0.5%
8823
 
0.4%
5422
 
0.4%
7622
 
0.4%
Other values (1708)5226
95.5%
ValueCountFrequency (%)
722
0.4%
825
0.5%
922
0.4%
1018
0.3%
1115
0.3%
1219
0.3%
1318
0.3%
1420
0.4%
1516
0.3%
1617
0.3%
ValueCountFrequency (%)
461331
< 0.1%
428211
< 0.1%
354991
< 0.1%
348741
< 0.1%
254001
< 0.1%
251631
< 0.1%
235471
< 0.1%
234571
< 0.1%
233011
< 0.1%
230921
< 0.1%

9
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct581
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean106.6628906
Minimum1
Maximum3212
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size42.9 KiB
2022-05-22T23:34:01.621362image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q117
median49
Q3126
95-th percentile408.4
Maximum3212
Range3211
Interquartile range (IQR)109

Descriptive statistics

Standard deviation167.3083617
Coefficient of variation (CV)1.56857142
Kurtosis60.95793459
Mean106.6628906
Median Absolute Deviation (MAD)38
Skewness5.504535353
Sum583766
Variance27992.08791
MonotonicityNot monotonic
2022-05-22T23:34:01.815593image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1256
 
4.7%
1491
 
1.7%
684
 
1.5%
279
 
1.4%
1177
 
1.4%
375
 
1.4%
875
 
1.4%
974
 
1.4%
1274
 
1.4%
1571
 
1.3%
Other values (571)4517
82.5%
ValueCountFrequency (%)
1256
4.7%
279
 
1.4%
375
 
1.4%
471
 
1.3%
555
 
1.0%
684
 
1.5%
762
 
1.1%
875
 
1.4%
974
 
1.4%
1067
 
1.2%
ValueCountFrequency (%)
32121
< 0.1%
29251
< 0.1%
23331
< 0.1%
22731
< 0.1%
18151
< 0.1%
17561
< 0.1%
16511
< 0.1%
16441
< 0.1%
16411
< 0.1%
16341
< 0.1%

10
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size42.9 KiB
1
4913 
2
 
329
5
 
115
4
 
88
3
 
28

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5473
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
14913
89.8%
2329
 
6.0%
5115
 
2.1%
488
 
1.6%
328
 
0.5%

Length

2022-05-22T23:34:01.983875image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-22T23:34:02.168967image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
14913
89.8%
2329
 
6.0%
5115
 
2.1%
488
 
1.6%
328
 
0.5%

Most occurring characters

ValueCountFrequency (%)
14913
89.8%
2329
 
6.0%
5115
 
2.1%
488
 
1.6%
328
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number5473
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
14913
89.8%
2329
 
6.0%
5115
 
2.1%
488
 
1.6%
328
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common5473
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
14913
89.8%
2329
 
6.0%
5115
 
2.1%
488
 
1.6%
328
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII5473
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
14913
89.8%
2329
 
6.0%
5115
 
2.1%
488
 
1.6%
328
 
0.5%

Interactions

2022-05-22T23:33:55.532339image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:40.364245image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:42.063183image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:43.638593image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:45.386631image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:46.978262image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:48.730561image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:50.403222image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:51.952792image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:53.582994image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:55.763297image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:40.558677image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:42.237575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:43.917994image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:45.544559image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:47.132629image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:48.893171image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:50.561990image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:52.107784image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:53.745459image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:55.936744image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:40.744448image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:42.415650image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:44.090653image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:45.694214image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:47.298065image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:49.043253image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:50.710656image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:52.258016image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:53.897578image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:56.164352image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:40.914179image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:42.586751image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:44.248593image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:45.849038image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:47.470523image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:49.301089image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:50.867890image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:52.419236image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:54.114733image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:56.358255image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:41.071280image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:42.741885image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:44.409568image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:46.002163image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:47.664357image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:49.450650image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:51.034764image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:52.594087image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:54.321376image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:56.515892image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:41.238836image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:42.888206image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:44.557933image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:46.159965image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:47.829339image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:49.620990image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:51.183236image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:52.756007image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:54.474057image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:56.690780image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:41.397307image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:43.041954image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:44.711271image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:46.307620image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:47.982380image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:49.796717image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:51.337816image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:52.944329image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:54.824777image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:56.845737image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:41.578254image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:43.189326image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:44.868366image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:46.455323image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:48.138285image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:49.954008image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:51.503089image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:53.103216image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:55.011433image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:57.002873image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:41.739038image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:43.341142image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:45.035951image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:46.615388image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:48.291866image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:50.108227image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:51.655916image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:53.272570image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:55.168092image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:57.155266image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:41.905574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:43.491891image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:45.192193image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:46.794809image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:48.438915image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:50.254671image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:51.803544image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:53.425998image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-22T23:33:55.326023image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-05-22T23:34:02.322544image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-22T23:34:02.543738image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-22T23:34:02.750450image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-22T23:34:02.954794image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-05-22T23:33:57.407618image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-05-22T23:33:57.612010image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

012345678910
057351.4000.4000.6572.33142361
167421.1670.4290.8813.60183751
26181083.0000.2870.7414.43318071
357351.4000.3710.7434.33132631
463180.5000.5000.9442.2591741
558401.6000.5501.0002.44224091
664240.6670.4170.7082.50101741
756301.2000.3330.33310.00101011
855251.0000.4000.52010.00101311
957351.4000.4860.9148.50173221

Last rows

012345678910
546311193212317.5450.2430.6831.5951614513251
54641202020.0000.8501.0008.50172022
54651878787.0000.9201.00016.00808752
54661279279279.0000.9641.00038.4326927972
54671161616.0001.0001.00016.00161612
546845242096131.0000.5420.60340.5711361264282
546974280.5710.7140.92910.00202621
547069557015.8330.3000.9111.641715191041
54717412875.8570.2130.8011.3661230451
54728180.1251.0001.0008.008814

Duplicate rows

Most frequently occurring

012345678910# duplicates
309190.1111.0001.0009.099148
268180.1251.0001.0008.088146
21888.0001.0001.0008.088125
143391.0000.7780.7787.077115
257170.1431.0001.0007.077145
153391.0000.8890.8898.088114
34131130.0771.0001.00013.01313144
01777.0001.0001.0007.077113
31101010.0001.0001.00010.01010123
81141414.0001.0001.00014.01414123